There's some discussion on what F-measure means. I understand that the beta parameter determines the weight of recall in the combined score. In specific one answer states that "for good models using the $F_{\beta}$ implies you consider false negatives $\beta^2$ times more costly than false positives." beta < 1
lends more weight to precision, while beta > 1
favors recall (beta -> 0
considers only precision, beta -> +inf
only recall).
If you want to weight precision or recall higher than the other, how do you decide on the beta? I'm a bit unclear on the math behind the F-measure, so does a beta = .5
mean that precision is weighted 2x as much as recall?